ManukaAnalysis
Melissa Taane
06/05/2019
F1 Layout
Map indicates the layout of the F1 generation resulting from a cross between EC201 and EC103 parents. Column 1 is approximately lengthways facing north.
“Map of F1 Trees”
Height Diagram
Diagram indicates the areas of leaf collection regarding height. Each Tree had 10 leaves collected, 3 from Low, 4 from Mid and 3 from High. The first leaf sampled was measured twice for replication comparison.
“Diagram of leaf collection levels”
Import and Arrange Data
“Full.xlsx” contains measurement information from sampling with the Dualex (https://www.force-a.com/en/capteurs-optiques-optical-sensors/dualex-scientific-chlorophyll-meter/), including;
Surface content of chlorophyll in ?g/cm? (Chl)
Epidermal Flavoid content in absorbance units; Flavonol(Flav) and Anthocyanin(Anth)
Nitrogen Balance Index status (NBI)
It also contains information about the block position, the leaf height information, and presense or absence of flowering
Sheet “Dup” contains only the samples that were replicated.
# Import Data Measures
Data <-read.xlsx("Full.xlsx", sheetName ="Full")
head(Data)## Collection.Day Allocation Block Column Row group Group.ID Tree.ID Rep.
## 1 4 CG 0 0 0 20 OG CG N
## 2 4 CG 0 0 0 20 OG CG N
## 3 4 CG 0 0 0 20 OG CG N
## 4 4 CG 0 0 0 20 OG CG N
## 5 4 CG 0 0 0 20 OG CG N
## 6 4 CG 0 0 0 20 OG CG N
## measure Height Flower Chl Flav Anth NBI
## 1 9 H Y 28.220 2.418 0.205 11.67
## 2 3 L Y 27.958 2.298 0.691 12.17
## 3 11 H Y 33.727 2.458 0.527 13.72
## 4 4 L Y 25.938 1.758 0.172 14.76
## 5 10 H Y 36.205 2.283 0.270 15.86
## 6 6 M Y 34.332 2.115 0.150 16.23Data$Column = as.factor(Data$Column)
Data$Row = as.factor(Data$Row)
# Import Replicate Data
Dup <-read.xlsx("Full.xlsx", sheetName ="Dup")
head(Dup)## Collection.Day group Group.ID Tree.ID Rep. measure Height Chl Flav
## 1 2.0 23 60 IN4DV Y1 1 L 1.916 2.363
## 2 1.5 3 1 IN4BT Y1 1 L 3.124 2.300
## 3 2.0 17 54 IN4DL Y2 2 L 3.414 1.826
## 4 1.5 32 28 IN4CP Y2 2 L 4.097 1.943
## 5 2.0 9 46 IN4DC Y1 1 L 4.909 1.928
## 6 1.5 5 3 IN4BW Y1 1 L 4.924 1.848
## Anth NBI
## 1 0.174 0.81
## 2 0.191 1.36
## 3 0.112 1.87
## 4 0.051 2.11
## 5 0.103 2.55
## 6 0.165 2.66#Isolate Crimson Glory Outgroup
CG = Data[c(1:11),]
#Isolate East Cape 201 Parent
EC201 = Data[c(12:21),]
#Isolate East Cape 103 Parent
EC103 = Data[c(22:33),]
#Isolate Offspring from the Parental Cross
F1 = Data[c(34:1825),]Replicate Analysis
Replicates were taken by measuring a single leaf sample from each tree twice, in order to establish consistency and reliability of measurements with the Dualex.
Replicate Data Overview
## Min. 1st Qu. Median Mean 3rd Qu.
## RepAnth 0.00100000 0.06500000 0.09850000 0.09799367 0.12525000
## RepChl 1.91600000 25.82475000 36.02700000 35.76269937 46.61000000
## RepFlav 1.05600000 1.76775000 1.99400000 1.96720570 2.19725000
## RepNBI 0.81000000 12.70250000 18.19000000 18.73398734 24.45750000
## Max.
## RepAnth 0.25400000
## RepChl 59.67400000
## RepFlav 2.71400000
## RepNBI 43.49000000Replicate Group means
## # A tibble: 2 x 5
## Rep. Anth Chl Flav NBI
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 Y1 0.0983 34.9 1.98 18.2
## 2 Y2 0.0977 36.6 1.95 19.2Anthocyanin Replicate Plots
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Chlorphyll Replicate Plots
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Flavonol Replicate Plots
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Nitrogen Balance Replicate Plots
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
There is no statistically significant differences between the two groups of measurements, this is a good sign indicative of the accuracy of the Dualex.
ANOVA of Replicates
## Analysis of Variance Table
##
## Response: Dup$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## Dup$Rep. 1 218 218.33 1.033 0.3102
## Residuals 314 66366 211.36## Analysis of Variance Table
##
## Response: Dup$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## Dup$Rep. 1 0.0521 0.052129 0.6506 0.4205
## Residuals 314 25.1596 0.080126## Analysis of Variance Table
##
## Response: Dup$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## Dup$Rep. 1 0.00003 0.0000304 0.0151 0.9023
## Residuals 314 0.63303 0.0020160## Analysis of Variance Table
##
## Response: Dup$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## Dup$Rep. 1 82.5 82.530 1.115 0.2918
## Residuals 314 23241.0 74.016The absence of statistically significant results indicates that our replicates are likely to be consistent.
Allocation Analysis
Allocation refers to which group measurements were taken from, i.e. A Parental Tree (EC103 or EC201), Outgroup Tree (CG), Parental Offspring (F1)
Allocation Data Overview
## Min. 1st Qu. Median Mean 3rd Qu.
## AllAnth 0.00100000 0.06800000 0.09600000 0.09772877 0.12400000
## AllChl 0.13000000 24.10200000 36.21000000 35.21443342 46.82300000
## AllFlav 1.05600000 1.80600000 1.97800000 1.96440493 2.12800000
## AllNBI 0.07000000 12.14000000 18.64000000 18.34807123 24.44000000
## Max.
## AllAnth 0.69100000
## AllChl 59.90500000
## AllFlav 2.86100000
## AllNBI 49.17000000Allocation Group Means
## # A tibble: 4 x 5
## Allocation Anth Chl Flav NBI
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 CG 0.258 36.0 2.17 16.7
## 2 EC103 0.0588 52.6 1.60 33.1
## 3 EC201 0.0779 52.6 1.71 31.7
## 4 F1 0.0971 35.0 1.97 18.2Boxplot comparing mean, median and measurement distributions of Allocations
Allocation ANOVAs
## Analysis of Variance Table
##
## Response: Data$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## Data$Allocation 3 6742 2247.21 9.9149 1.748e-06 ***
## Residuals 1821 412728 226.65
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Variance Table
##
## Response: Data$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## Data$Allocation 3 6742 2247.21 9.9149 1.748e-06 ***
## Residuals 1821 412728 226.65
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Variance Table
##
## Response: Data$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## Data$Allocation 3 2.713 0.90423 14.755 1.708e-09 ***
## Residuals 1821 111.598 0.06128
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Variance Table
##
## Response: Data$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## Data$Allocation 3 0.3040 0.101343 47.474 < 2.2e-16 ***
## Residuals 1821 3.8873 0.002135
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1F1 Samples (Approx 3 years) appear more similar to that of the Crimson Glory plant than either/both parents - this is possibly due to age effects as CG is likely more similar in this regard being shorter (No age confirmed). ANOVAs incidate there is significant differences between the allocations - this is to be expected.
Parent Tree Analysis
Parent Tree Means
## # A tibble: 2 x 5
## Tree.ID Anth Chl Flav NBI
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 EC103 0.0588 52.6 1.60 33.1
## 2 EC201 0.0779 52.6 1.71 31.7Parent Tree Data Overview
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## ParAnth 0.004000 0.048250 0.058000 0.067500 0.086750 0.156000
## ParChl 30.722000 50.356750 55.739000 52.598909 57.794750 59.757000
## ParFlav 1.293000 1.475000 1.594000 1.650273 1.821000 2.138000
## ParNBI 14.370000 29.872500 34.025000 32.481818 35.892500 44.880000Anthocyanin Parent Plots
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Chlorophyll Parent Plots
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Flavonol Parent Plots
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Nitrogen Parent Plots
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Parent t.tests
##
## Welch Two Sample t-test
##
## data: Parent$Anth by Parent$Tree.ID
## t = -1.2283, df = 19.107, p-value = 0.2342
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.05154327 0.01340994
## sample estimates:
## mean in group EC103 mean in group EC201
## 0.05883333 0.07790000##
## Welch Two Sample t-test
##
## data: Parent$Chl by Parent$Tree.ID
## t = -0.003842, df = 17.804, p-value = 0.997
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -7.830954 7.802387
## sample estimates:
## mean in group EC103 mean in group EC201
## 52.59242 52.60670##
## Welch Two Sample t-test
##
## data: Parent$Flav by Parent$Tree.ID
## t = -1.141, df = 19.612, p-value = 0.2676
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.31409194 0.09215861
## sample estimates:
## mean in group EC103 mean in group EC201
## 1.599833 1.710800##
## Welch Two Sample t-test
##
## data: Parent$NBI by Parent$Tree.ID
## t = 0.47573, df = 14.7, p-value = 0.6413
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.076667 7.987334
## sample estimates:
## mean in group EC103 mean in group EC201
## 33.14333 31.68800Interesting sample pattern here, Chl and NBI start low and work high, Flav does the opposite. Maybe accuracy of measurements?
Parental Cross Analysis (F1 Generation)
A Brief Look at Some Tree Data
## # A tibble: 6 x 5
## Tree.ID Chl NBI Anth Flav
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 IN4BT 34.5 16.2 0.110 2.17
## 2 IN4BV 27.5 13.8 0.121 2.01
## 3 IN4BW 24.8 12.5 0.127 1.99
## 4 IN4BX 37.6 18.4 0.0905 2.05
## 5 IN4BY 24.2 12.5 0.110 1.98
## 6 IN4BZ 32.0 15.6 0.106 2.09Anthocyanin Overview
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: F1$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Tree.ID 158 0.5253 0.0033247 1.8093 2.001e-08 ***
## Residuals 1633 3.0007 0.0018376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Chlorophyll Overview
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: F1$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Tree.ID 158 47130 298.29 1.3395 0.004474 **
## Residuals 1633 363634 222.68
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Flavonol Overview
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: F1$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Tree.ID 158 25.118 0.15897 3.0531 < 2.2e-16 ***
## Residuals 1633 85.030 0.05207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Nitrogen Overview
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: F1$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Tree.ID 158 16378 103.661 1.5147 8.435e-05 ***
## Residuals 1633 111761 68.439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A Closer Look at Invidual F1 Trees and Their Measurements
Msr = group_by(F1, Tree.ID, measure)
Msr = summarise(Msr, Anth = mean(Anth), Flav = mean(Flav), Chl = mean(Chl),NBI = mean(NBI))
#Select Random Column
#sample(1:4,10, replace = T)
#[1] 3 3 2 1 1 4 1 2 4 3
#Select Random Row
#sample(1:50,10, replace = T)
#[1] 34 37 44 20 44 47 9 19 22 40IN4G5
IN4EP
IN4EY
IN4CD
IN4D8
IN4GV
IN4C2
IN4E3
IN4HK
IN4GC
F1 Height Analysis
## # A tibble: 3 x 5
## Height Anth Chl Flav NBI
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 H 0.105 33.9 1.93 17.9
## 2 L 0.0968 35.6 1.98 18.5
## 3 M 0.0913 35.3 1.98 18.1
Height Measure Distributions
Anthocyanin
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Chlorophyll
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Flavonol
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Nitrogen
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: F1$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 0.0526 0.0262759 13.533 1.468e-06 ***
## Residuals 1789 3.4735 0.0019416
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Variance Table
##
## Response: F1$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 853 426.30 1.8605 0.1559
## Residuals 1789 409911 229.13## Analysis of Variance Table
##
## Response: F1$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 0.915 0.45758 7.4943 0.0005739 ***
## Residuals 1789 109.232 0.06106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Variance Table
##
## Response: F1$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 100 50.044 0.6992 0.4971
## Residuals 1789 128039 71.570Row Analysis
Sample of Row Means
## # A tibble: 6 x 5
## Row Chl NBI Anth Flav
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 1 35.6 17.3 0.107 2.09
## 2 10 30.5 15.7 0.112 1.98
## 3 11 29.9 15.0 0.112 2.04
## 4 12 34.6 18.7 0.0968 1.84
## 5 13 31.3 17.5 0.107 1.86
## 6 14 35.0 17.9 0.0888 1.98Investigating Differences Between Rows with 2,3 and 4 Trees.
Summary of Rows with 2 Trees
## Row Chl NBI Anth
## 17 :1 Min. :32.13 Min. :17.14 Min. :0.1163
## 0 :0 1st Qu.:32.13 1st Qu.:17.14 1st Qu.:0.1163
## 1 :0 Median :32.13 Median :17.14 Median :0.1163
## 10 :0 Mean :32.13 Mean :17.14 Mean :0.1163
## 11 :0 3rd Qu.:32.13 3rd Qu.:17.14 3rd Qu.:0.1163
## 12 :0 Max. :32.13 Max. :17.14 Max. :0.1163
## (Other):0
## Flav
## Min. :1.887
## 1st Qu.:1.887
## Median :1.887
## Mean :1.887
## 3rd Qu.:1.887
## Max. :1.887
## Summary of Rows with 3 Trees
## Row Chl NBI Anth
## 1 : 1 Min. :29.19 Min. :14.38 Min. :0.07868
## 10 : 1 1st Qu.:32.02 1st Qu.:16.44 1st Qu.:0.08535
## 11 : 1 Median :34.38 Median :17.81 Median :0.09354
## 14 : 1 Mean :34.97 Mean :18.12 Mean :0.09677
## 15 : 1 3rd Qu.:37.05 3rd Qu.:19.46 3rd Qu.:0.10917
## 16 : 1 Max. :43.89 Max. :23.16 Max. :0.12239
## (Other):33
## Flav
## Min. :1.820
## 1st Qu.:1.931
## Median :1.974
## Mean :1.973
## 3rd Qu.:2.037
## Max. :2.092
## Summary of Rows with 4 Trees
## Row Chl NBI Anth
## 12 :1 Min. :31.33 Min. :16.99 Min. :0.08183
## 13 :1 1st Qu.:34.20 1st Qu.:17.74 1st Qu.:0.09326
## 23 :1 Median :35.08 Median :18.29 Median :0.09747
## 32 :1 Mean :35.09 Mean :18.48 Mean :0.09702
## 41 :1 3rd Qu.:36.36 3rd Qu.:19.10 3rd Qu.:0.10265
## 45 :1 Max. :38.42 Max. :20.71 Max. :0.10721
## (Other):4
## Flav
## Min. :1.839
## 1st Qu.:1.898
## Median :1.944
## Mean :1.941
## 3rd Qu.:1.995
## Max. :2.032
## Looking at Row Differences in Variance
## [1] 0.0001562666## [1] 6.039853e-05## [1] 14.76679## [1] 4.10476## [1] 0.004848629## [1] 0.004125519## [1] 5.602987## [1] 1.155704T.Tests to Compare Similarity Between Rows with 3 and Rows with 4 Trees.
##
## Welch Two Sample t-test
##
## data: R4$Anth and R3$Anth
## t = 0.081174, df = 22.551, p-value = 0.936
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.006306855 0.006821440
## sample estimates:
## mean of x mean of y
## 0.09702315 0.09676586##
## Welch Two Sample t-test
##
## data: R4$Chl and R3$Chl
## t = 0.13309, df = 27.683, p-value = 0.8951
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.702350 1.938809
## sample estimates:
## mean of x mean of y
## 35.08876 34.97053##
## Welch Two Sample t-test
##
## data: R4$Flav and R3$Flav
## t = -1.3877, df = 14.921, p-value = 0.1856
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.08156262 0.01725693
## sample estimates:
## mean of x mean of y
## 1.941185 1.973338##
## Welch Two Sample t-test
##
## data: R4$Flav and R3$Flav
## t = -1.3877, df = 14.921, p-value = 0.1856
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.08156262 0.01725693
## sample estimates:
## mean of x mean of y
## 1.941185 1.973338There appears to be no signficant differences between Rows with 3 trees and rows with 4 trees for any of the measures.
Row Plots and ANOVAs
Anthocyanin
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Chlorophyll
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Flavonol
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Nitrogen
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Column Analysis
## # A tibble: 4 x 5
## Column Chl NBI Anth Flav
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 1 33.8 17.2 0.0945 2.01
## 2 2 35.4 18.6 0.0992 1.94
## 3 3 36.0 18.7 0.0990 1.96
## 4 4 34.3 18.7 0.0912 1.88## Analysis of Variance Table
##
## Response: F1$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Column 3 0.0122 0.0040655 2.0687 0.1024
## Residuals 1788 3.5138 0.0019652## Analysis of Variance Table
##
## Response: F1$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Column 3 1586 528.66 2.3101 0.07453 .
## Residuals 1788 409178 228.85
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Variance Table
##
## Response: F1$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Column 3 2.305 0.76823 12.737 3.087e-08 ***
## Residuals 1788 107.843 0.06031
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Variance Table
##
## Response: F1$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Column 3 821 273.626 3.8427 0.009333 **
## Residuals 1788 127318 71.207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: F1$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 0.05255 0.0262759 14.9537 3.788e-07 ***
## F1$Column 3 0.01228 0.0040942 2.3300 0.0727244 .
## F1$Row 49 0.23127 0.0047199 2.6861 6.175e-09 ***
## F1$Height:F1$Column 6 0.02055 0.0034247 1.9490 0.0699958 .
## F1$Height:F1$Row 98 0.20024 0.0020433 1.1628 0.1394606
## F1$Column:F1$Row 106 0.28473 0.0026861 1.5287 0.0006952 ***
## F1$Height:F1$Column:F1$Row 212 0.41375 0.0019517 1.1107 0.1490621
## Residuals 1315 2.31066 0.0017572
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Variance Table
##
## Response: F1$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 0.05255 0.0262759 14.2135 7.586e-07 ***
## F1$Row 49 0.23404 0.0047763 2.5837 2.178e-08 ***
## F1$Height:F1$Row 98 0.20394 0.0020810 1.1257 0.1942
## Residuals 1642 3.03551 0.0018487
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Variance Table
##
## Response: F1$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 853 426.30 2.0422 0.130152
## F1$Column 3 1577 525.57 2.5178 0.056677 .
## F1$Row 49 21813 445.16 2.1326 1.244e-05 ***
## F1$Height:F1$Column 6 1282 213.61 1.0233 0.408244
## F1$Height:F1$Row 98 27033 275.85 1.3215 0.022708 *
## F1$Column:F1$Row 106 24037 226.77 1.0863 0.265386
## F1$Height:F1$Column:F1$Row 212 59670 281.46 1.3484 0.001427 **
## Residuals 1315 274499 208.74
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Variance Table
##
## Response: F1$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 853 426.30 1.9372 0.14444
## F1$Row 49 21279 434.27 1.9734 8.34e-05 ***
## F1$Height:F1$Row 98 27286 278.43 1.2652 0.04464 *
## Residuals 1642 361346 220.06
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Variance Table
##
## Response: F1$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Row 49 21394 436.61 1.9533 0.0001046 ***
## Residuals 1742 389370 223.52
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Variance Table
##
## Response: F1$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 0.915 0.45758 9.3660 9.143e-05 ***
## F1$Column 3 2.323 0.77447 15.8523 3.957e-10 ***
## F1$Row 49 8.139 0.16611 3.4001 1.213e-13 ***
## F1$Height:F1$Column 6 1.004 0.16728 3.4240 0.002334 **
## F1$Height:F1$Row 98 5.743 0.05860 1.1995 0.096365 .
## F1$Column:F1$Row 106 14.790 0.13953 2.8559 < 2.2e-16 ***
## F1$Height:F1$Column:F1$Row 212 12.988 0.06126 1.2540 0.012358 *
## Residuals 1315 64.245 0.04886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Variance Table
##
## Response: F1$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 100 50.044 0.7850 0.4563286
## F1$Column 3 818 272.802 4.2793 0.0051365 **
## F1$Row 49 7816 159.511 2.5021 8.551e-08 ***
## F1$Height:F1$Column 6 792 132.004 2.0707 0.0539220 .
## F1$Height:F1$Row 98 8175 83.418 1.3085 0.0268723 *
## F1$Column:F1$Row 106 7765 73.250 1.1490 0.1510179
## F1$Height:F1$Column:F1$Row 212 18842 88.877 1.3942 0.0004396 ***
## Residuals 1315 83831 63.750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Variance Table
##
## Response: F1$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Row 49 7866 160.527 2.3456 6.383e-07 ***
## F1$Column 3 789 263.021 3.8432 0.00934 **
## F1$Row:F1$Column 106 7724 72.864 1.0647 0.31303
## Residuals 1633 111761 68.439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Controlling for Height,Column,Row effects
Dat = F1
#Total Mean
Dat$ATmean = mean(Dat$Anth)
Dat$CTmean = mean(Dat$Chl)
Dat$FTmean = mean(Dat$Flav)
Dat$NTmean = mean(Dat$NBI)
#Remove Total Mean
Dat$Anth2 = Dat$Anth - Dat$ATmean
Dat$Chl2 = Dat$Chl - Dat$CTmean
Dat$Flav2 = Dat$Flav - Dat$FTmean
Dat$NBI2 = Dat$NBI - Dat$NTmean
#Height Mean
DatH = group_by(Dat, Height)
DatH = summarise(DatH, HAnth = mean(Anth, na.rm = T), HFlav = mean(Flav,na.rm = T))
#Add Height Mean to Data
Dat =merge(Dat, DatH, by.x = "Height")
#Calculate Height Mean Deviation from Total Mean
Dat$ATH <- Dat$ATmean - Dat$HAnth
Dat$FTH = Dat$FTmean - Dat$HFlav
#Controlling for Height Anth and Flav
Dat$Anth3 = Dat$Anth2 + Dat$ATH
Dat$Flav3 = Dat$Flav2 + Dat$FTH
#Column Mean
DatC = group_by(Dat, Column)
DatC = summarise(DatC, CFlav = mean(Flav,na.rm = T), CNBI = mean(NBI,na.rm = T))
#Add Height Mean to Data
Dat =merge(Dat, DatC, by.x = "Column")
#Calculate Height Mean Deviation from Total Mean
Dat$FTH = Dat$FTmean - Dat$CFlav
Dat$NTH = Dat$NTmean - Dat$CNBI
#Controlling for Column Flav and NBI
Dat$Flav4 = Dat$Flav3 + Dat$FTH
Dat$NBI3 = Dat$NBI2 + Dat$NTH
#Row Mean
DatR = group_by(Dat, Row)
DatR = summarise(DatR, RAnth = mean(Anth,na.rm = T),RChl = mean(Chl,na.rm = T), RFlav = mean(Flav,na.rm = T), RNBI = mean(NBI,na.rm = T))
#Add Row Mean to Data
Dat =merge(Dat, DatR, by.x = "Row")
#Calculate Row Mean Deviation from Total Mean
Dat$ATR = Dat$ATmean - Dat$RAnth
Dat$CTR = Dat$CTmean - Dat$RChl
Dat$FTR = Dat$FTmean - Dat$RFlav
Dat$NTR = Dat$NTmean - Dat$RNBI
#Controlling for Row in Anth, Chl, Flav and NBI
Dat$Anth4 = Dat$Anth3 + Dat$ATR
Dat$Chl3 = Dat$Chl2 + Dat$CTR
Dat$Flav5 = Dat$Flav4 + Dat$FTR
Dat$NBI4 = Dat$NBI3 + Dat$NTR
#Anova Checks
AnAnth = lm(Dat$Anth4 ~ Dat$Height*Dat$Column*Dat$Row)
anova(AnAnth)## Analysis of Variance Table
##
## Response: Dat$Anth4
## Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Height 2 0.00000 0.0000012 0.0007 0.9993236
## Dat$Column 3 0.00900 0.0029994 1.7070 0.1637135
## Dat$Row 49 0.00061 0.0000125 0.0071 1.0000000
## Dat$Height:Dat$Column 6 0.02055 0.0034247 1.9490 0.0699958 .
## Dat$Height:Dat$Row 98 0.20024 0.0020433 1.1628 0.1394606
## Dat$Column:Dat$Row 106 0.28473 0.0026861 1.5287 0.0006952 ***
## Dat$Height:Dat$Column:Dat$Row 212 0.41375 0.0019517 1.1107 0.1490621
## Residuals 1315 2.31066 0.0017572
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1AnChl = lm(Dat$Chl3 ~ Dat$Height*Dat$Column*Dat$Row)
anova(AnChl)## Analysis of Variance Table
##
## Response: Dat$Chl3
## Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Height 2 737 368.65 1.7660 0.171413
## Dat$Column 3 1956 652.10 3.1239 0.025073 *
## Dat$Row 49 155 3.17 0.0152 1.000000
## Dat$Height:Dat$Column 6 1282 213.61 1.0233 0.408244
## Dat$Height:Dat$Row 98 27033 275.85 1.3215 0.022708 *
## Dat$Column:Dat$Row 106 24037 226.77 1.0863 0.265386
## Dat$Height:Dat$Column:Dat$Row 212 59670 281.46 1.3484 0.001427 **
## Residuals 1315 274499 208.74
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1AnFlav = lm(Dat$Flav5 ~ Dat$Height*Dat$Column*Dat$Row)
anova(AnFlav)## Analysis of Variance Table
##
## Response: Dat$Flav5
## Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Height 2 0.001 0.000491 0.0101 0.989993
## Dat$Column 3 0.012 0.004029 0.0825 0.969582
## Dat$Row 49 0.215 0.004387 0.0898 1.000000
## Dat$Height:Dat$Column 6 1.004 0.167281 3.4240 0.002334 **
## Dat$Height:Dat$Row 98 5.743 0.058602 1.1995 0.096365 .
## Dat$Column:Dat$Row 106 14.790 0.139527 2.8559 < 2.2e-16 ***
## Dat$Height:Dat$Column:Dat$Row 212 12.988 0.061264 1.2540 0.012358 *
## Residuals 1315 64.245 0.048855
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1AnNBI = lm(Dat$NBI4 ~ Dat$Height*Dat$Column*Dat$Row)
anova(AnNBI)## Analysis of Variance Table
##
## Response: Dat$NBI4
## Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Height 2 80 39.936 0.6264 0.5346458
## Dat$Column 3 50 16.555 0.2597 0.8544529
## Dat$Row 49 22 0.451 0.0071 1.0000000
## Dat$Height:Dat$Column 6 792 132.004 2.0707 0.0539220 .
## Dat$Height:Dat$Row 98 8175 83.418 1.3085 0.0268723 *
## Dat$Column:Dat$Row 106 7765 73.250 1.1490 0.1510179
## Dat$Height:Dat$Column:Dat$Row 212 18842 88.877 1.3942 0.0004396 ***
## Residuals 1315 83831 63.750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#New Heatmaps
#DatHM = group_by(Dat, Column, Row)
#DatHM = summarise(DatHM, Anth = mean(Anth4,na.rm = T), Chl = mean(Chl3,na.rm = T), Flav = mean(Flav5,na.rm = T), NBI = mean(NBI4,na.rm = T))
#write.xlsx(as.data.frame(DatHM), file = "DatHM.xlsx", sheetName ="Dat",col.names = T, row.names = F)
#Anthocyanin Heatmaps
AnthDoc <-read.xlsx("Anth.xlsx", sheetName = "Anth")
heatmaply(AnthDoc, xlab = "Column",ylab ="Row", Rowv = FALSE, Colv = FALSE, main = "Anth Before Correction")AnthDoc2 <-read.xlsx("DatHM2.xlsx", sheetName = "Anth")
heatmaply(AnthDoc2, xlab = "Column",ylab ="Row", Rowv = FALSE, Colv = FALSE, main = "Anth After Correction")#Chlorophyll Heatmaps
ChlDoc<-read.xlsx("Chl.xlsx", sheetName = "Chl")
heatmaply(ChlDoc, xlab = "Column",ylab ="Row", Rowv = FALSE, Colv = FALSE, main = "Chl Before Correction")ChlDoc2 <-read.xlsx("DatHM2.xlsx", sheetName = "Chl")
heatmaply(ChlDoc2, xlab = "Column",ylab ="Row", Rowv = FALSE, Colv = FALSE, main = "Chl After Correction")#Flavonol Heatmaps
FlavDoc<-read.xlsx("Flav.xlsx", sheetName = "Flav")
heatmaply(FlavDoc, xlab = "Column",ylab ="Row", Rowv = FALSE, Colv = FALSE, main = "Flav Before Correction")FlavDoc2 <-read.xlsx("DatHM2.xlsx", sheetName = "Flav")
heatmaply(FlavDoc2, xlab = "Column",ylab ="Row", Rowv = FALSE, Colv = FALSE, main = "Flav After Correction")#NBI Heatmaps
NBIDoc<-read.xlsx("NBI.xlsx", sheetName = "NBI")
heatmaply(NBIDoc, xlab = "Column",ylab ="Row", Rowv = FALSE, Colv = FALSE, main = "NBI Before Correction")NBIDoc2 <-read.xlsx("DatHM2.xlsx", sheetName = "NBI")
heatmaply(NBIDoc2, xlab = "Column",ylab ="Row", Rowv = FALSE, Colv = FALSE, main = "NBI After Correction")Looking at controlled data
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Correlations Between Measures
Comparing correlations between the 4 dualex measures
## Analysis of Variance Table
##
## Response: Anth4
## Df Sum Sq Mean Sq F value Pr(>F)
## Chl3 1 0.30271 0.302706 192.4807 < 2.2e-16 ***
## Flav5 1 0.05792 0.057917 36.8277 1.572e-09 ***
## NBI4 1 0.01331 0.013306 8.4608 0.0036737 **
## Chl3:Flav5 1 0.02196 0.021955 13.9607 0.0001925 ***
## Chl3:NBI4 1 0.03008 0.030085 19.1299 1.292e-05 ***
## Flav5:NBI4 1 0.00791 0.007905 5.0266 0.0250834 *
## Chl3:Flav5:NBI4 1 0.00005 0.000055 0.0348 0.8519736
## Residuals 1784 2.80562 0.001573
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Variance Table
##
## Response: Chl3
## Df Sum Sq Mean Sq F value Pr(>F)
## Anth4 1 36383 36383 1.9357e+04 < 2.2e-16 ***
## Flav5 1 383 383 2.0354e+02 < 2.2e-16 ***
## NBI4 1 342014 342014 1.8196e+05 < 2.2e-16 ***
## Anth4:Flav5 1 569 569 3.0285e+02 < 2.2e-16 ***
## Anth4:NBI4 1 115 115 6.1416e+01 7.879e-15 ***
## Flav5:NBI4 1 6552 6552 3.4859e+03 < 2.2e-16 ***
## Anth4:Flav5:NBI4 1 1 1 3.2830e-01 0.5667
## Residuals 1784 3353 2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Variance Table
##
## Response: Flav5
## Df Sum Sq Mean Sq F value Pr(>F)
## Anth4 1 2.455 2.455 198.4244 < 2.2e-16 ***
## Chl3 1 0.105 0.105 8.4580 0.003679 **
## NBI4 1 73.486 73.486 5940.1653 < 2.2e-16 ***
## Anth4:Chl3 1 0.438 0.438 35.4071 3.213e-09 ***
## Anth4:NBI4 1 0.121 0.121 9.7847 0.001788 **
## Chl3:NBI4 1 0.295 0.295 23.8417 1.139e-06 ***
## Anth4:Chl3:NBI4 1 0.028 0.028 2.2925 0.130175
## Residuals 1784 22.070 0.012
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Variance Table
##
## Response: NBI4
## Df Sum Sq Mean Sq F value Pr(>F)
## Anth4 1 12013 12013 1.5967e+04 < 2.2e-16 ***
## Chl3 1 95398 95398 1.2679e+05 < 2.2e-16 ***
## Flav5 1 9255 9255 1.2300e+04 < 2.2e-16 ***
## Anth4:Chl3 1 36 36 4.7348e+01 8.199e-12 ***
## Anth4:Flav5 1 118 118 1.5676e+02 < 2.2e-16 ***
## Chl3:Flav5 1 1393 1393 1.8521e+03 < 2.2e-16 ***
## Anth4:Chl3:Flav5 1 1 1 1.6591e+00 0.1979
## Residuals 1784 1342 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Flowering
## Analysis of Variance Table
##
## Response: Dat$Anth4
## Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Flower 1 0.0002 0.00023977 0.1325 0.7159
## Residuals 1790 3.2393 0.00180967## Analysis of Variance Table
##
## Response: Dat$Chl3
## Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Flower 1 37 36.997 0.1701 0.6801
## Residuals 1790 389333 217.505## Analysis of Variance Table
##
## Response: Dat$Flav5
## Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Flower 1 0.248 0.247514 4.4866 0.0343 *
## Residuals 1790 98.750 0.055168
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Variance Table
##
## Response: Dat$NBI4
## Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Flower 1 12 11.938 0.1788 0.6725
## Residuals 1790 119544 66.784